Quality of drug interaction alerts in prescribing and dispensing software

Michelle Sweidan, James F Reeve, Jo-anne E Brien, Pradeep Jayasuriya, Jennifer H Martin and Graeme M Vernon
Med J Aust 2009; 190 (5): 251-254. || doi: 10.5694/j.1326-5377.2009.tb02387.x
Published online: 2 March 2009


Objective: To investigate the quality of drug interaction decision support in selected prescribing and dispensing software systems, and to compare this information with that found in a range of reference sources.

Design and setting: A comparative study, conducted between June 2006 and February 2007, of the support provided for making decisions about 20 major and 20 minor drug interactions in six prescribing and three dispensing software systems used in primary care in Australia. Five electronic reference sources were evaluated for comparison.

Main outcome measures: Sensitivity, specificity and quality of information; for major interactions: whether information on clinical effects, timeframe and pharmacological mechanism was included, whether management advice was helpful, and succinctness.

Results: Six of the nine software systems had a sensitivity rate ≥ 90%, detecting most of the major interactions. Only 3/9 systems had a specificity rate of ≥ 80%, with other systems providing inappropriate or unhelpful alerts for many minor interactions. Only 2/9 systems provided adequate information about clinical effects for more than half the major drug interactions, and 1/9 provided useful management advice for more than half of these. The reference sources had high sensitivity and in general provided more comprehensive clinical information than the software systems.

Conclusions: Drug interaction decision support in commonly used prescribing and dispensing software has significant shortcomings.

Drug interaction alerts are a basic form of electronic decision support in clinical software, usually in the form of a “pop-up” message that appears when interacting drugs are prescribed or dispensed. Drug interaction decision support has considerable potential for improving patient safety, but there is concern about variation between systems1 and about the quality and usefulness of the information. General practitioners and pharmacists in Australia have complained about irrelevant drug interaction alerts, and lack confidence in the comprehensiveness and accuracy of the information.2,3 Studies have identified problems relating to the quality, relevance and useability of information in interaction alerts4-7 and inconsistencies between references on drug interactions.8,9

To be useful, a drug interaction decision support system must be both sensitive (to alert the user to potential clinically significant interactions) and specific (to avoid inundation with irrelevant alerts). In Australia, there are no standards for clinical software10 or for the integration of decision support tools into software,11 nor is there a national drug interaction database that software vendors can use (as there is in the Netherlands12).

We investigated the drug interaction decision support in prescribing and dispensing software used in primary care in Australia, to examine the quality and usefulness of the information, to compare the information in alerts with that found in a range of reference sources, and to investigate variations between systems, including sensitivity and specificity.


The study was conducted between June 2006 and February 2007. The researchers (M S and J F R) worked with an expert panel comprising a clinical–academic pharmacist (J E B), a GP (P J), a clinical pharmacologist–physician (J H M) and a drug information pharmacist (G M V). The panel’s role was to comment on the study design, assess the data, and make recommendations.

Software systems and reference sources

Six prescribing systems — Best Practice (Best Practice Software, Bundaberg, Qld), Genie (Genie Solutions, Brisbane, Qld), Medical Director 2 (Health Communication Network, Sydney, NSW), MedTech32 (Medtech Global, Melbourne, Vic), Plexus (IBA Health, Sydney, NSW) and Profile (Intrahealth Systems, Sydney, NSW) — and three dispensing systems — Amfac Dispense (Corum Health Services, Sydney, NSW), Pharmasol LOTS (Corum Health Services, Sydney, NSW) and WiniFRED (PCA NU Systems, Melbourne, Vic) — were selected. It was estimated that 80%–90% of GPs3,13 and 65% of community pharmacists (Mike Farrell, Councillor, Pharmacy Guild of Australia, personal communication) in Australia used one of these systems.

Five electronic reference sources were selected for comparison — three Australian publications commonly used by doctors and pharmacists, and two publications from the United Kingdom. The Australian Medicines Handbook14 contains brief information on drug interactions, which is intended to be used in conjunction with the full drug monographs in the handbook. MIMS DrugAlert Interactions15 is a specialist knowledgebase on drug interactions that is integrated into some software systems. Product information15 is the official source of drug information in Australia, and is approved by the Therapeutic Goods Administration. Stockley’s Drug Interactions,16 published in the UK, is a comprehensive source widely used by drug information pharmacists, with a companion clinical decision support tool, Stockley’s Interaction Alerts.17

Selection of drug interactions

Twenty “major” (defined here as clinically significant) and 20 “minor” (clinically unimportant) drug interactions were assessed in each software system (Box 1). The selection criteria for major interactions were: both drugs used in primary care + pair is potentially used in combination + at least one drug in pair is commonly used or is a new drug or the pair must produce a potentially serious adverse outcome + interaction is classified as “moderate” or “severe” in Stockley’s Interaction Alerts17 and “moderate” or “major” in Drug Interaction Facts.18 Selection criteria for minor interactions were: both drugs are used in primary care + pair is potentially used in combination + at least one drug in pair is commonly used or is a new drug + interaction is classified as “minor” in Drug Interaction Facts18 OR one drug is a topical product and topical use is unlikely to result in a clinically significant interaction.

The major interactions represent a range encountered in practice, not necessarily those that are most common or most severe. Potential drug pairs (47 major and 51 minor interactions) were identified on the basis of our clinical experience, reports to the Adverse Drug Reactions Advisory Committee, similar published studies, and whether the drugs are commonly prescribed.19 A modified Delphi process with the expert panel members was used to remove drug pairs to produce the 40 interactions for testing.

Data assessment

Sensitivity and specificity were calculated similarly to other studies investigating drug interaction software.20,21 Sensitivity was defined as the proportion of the 20 major interactions detected (ie, triggering an alert in the software system or noted in the reference source). Specificity was defined as the proportion of the 20 minor interactions not detected (ie, not triggering an alert in the software or noted in the reference source), or detected but including an explanation of why the interaction is not important in clinical practice.


Five of the six prescribing systems had high sensitivity, detecting ≥ 90% of the major drug interactions (Box 2). The systems were more variable and generally performed less well with minor interactions, with specificity ranging from 25% to 85%. For example, in the system with a specificity of 25%, alerts came up for 75% (15/20) of the minor interactions, and the information provided did not indicate that the interaction was not important in clinical practice.

Only one of the three dispensing systems detected ≥ 90% of the major interactions (Box 2). In general, their specificity was better than the prescribing systems (range, 65%–85%).

Overall, the reference sources had higher sensitivity and specificity than the software systems. They all detected ≥ 90% of the major interactions, and specificity ranged from 65% to 90%.

Information quality and comprehensiveness

Only two of the nine software systems provided adequate information about clinical effects for more than half of the major drug interactions (Box 3). Little or no information about the timeframe of interactions was provided, even in a comprehensive reference source such as Stockley’s Drug Interactions, which had this information for 11/20 interactions. Only one software system described the pharmacological mechanism for most interactions (15/20). Overall, the software systems did not provide useful management advice, most providing advice considered “somewhat useful” or none at all. Four software systems provided links to additional information: two to the product information, and two to more detailed information from the drug interactions knowledgebase used. This information was not assessed.

In some cases, the language used in alerts lacked clarity and succinctness, and was occasionally incomprehensible. Some systems used unhelpful phrases such as “use with caution”, spelled drug names incorrectly, or named drugs that are not available in Australia.

In general, the reference sources performed better than the software systems in terms of the comprehensiveness of information provided (Box 3).


Most of the prescribing and dispensing software systems assessed did not offer consistently useful, relevant support to GPs and pharmacists for making decisions about potentially interacting drugs. The systems varied markedly, due in part to the use of different knowledgebases but also to variable use of the same knowledgebase. Four of the prescribing systems used MIMS DrugAlert Interactions, but different implementations produced markedly different results.

It is cause for concern that three of the nine systems detected < 90% of major interactions. The relatively low specificity rates (< 80% for six systems) suggest that some software systems contain many inappropriate or unhelpful alerts. Examples are inappropriate alerts for interactions with topical products (eg, terbinafine cream + warfarin), for minor pharmacokinetic interactions (eg, warfarin + spironolactone), and failure to differentiate between drugs in a class (eg, digoxin + felodipine versus other calcium channel blockers).

There were many gaps in the information considered necessary for decision making, including clinical effects and management advice.

This study had some limitations. The panel members were selected for their expert knowledge and clinical experience, but they are not necessarily representative of software users. The rating of information was subjective, although a consistent approach was used and consensus was achieved.

Our results support international literature in this area. Studies have shown variation and deficiencies in drug interaction decision support in pharmacy software systems in the United States,22 in clinical software systems used in primary care in Canada,23 and in drug interaction software for personal digital assistants.20,21 A study of German GPs found that they wanted detailed drug interaction information, but their needs were not being met for some components (eg, management of interactions, outcomes).24

The expert panel has made recommendations to publishers of drug interaction information and software vendors to improve drug interaction decision support (Box 4). Most software vendors rely on the drug interaction knowledgebases they purchase, so publishers must ensure provision of accurate, useful, up-to-date information, and vendors should implement the information appropriately. Standards or guidelines for decision support in software could improve quality and result in greater consistency between software systems.

Deficiencies in drug interaction decision support can impede the quality use of medicines, in terms of individual patient management and by causing alert fatigue, desensitising users to prompts and alerts. It is important that doctors and pharmacists are aware of the limitations of the decision support features in their software systems, including the quality and source of the underlying evidence, and refer to alternative sources of information if required.

4 Recommendations for drug interaction alerts in prescribing and dispensing software


Format and language

  • Michelle Sweidan1
  • James F Reeve1
  • Jo-anne E Brien2,3
  • Pradeep Jayasuriya4
  • Jennifer H Martin5,6
  • Graeme M Vernon7

  • 1 Pharmaceutical Decision Support Program, National Prescribing Service, Melbourne, VIC.
  • 2 University of Sydney, Sydney, NSW.
  • 3 University of New South Wales, Sydney, NSW.
  • 4 Belgravia Medical Centre, Perth, WA.
  • 5 Departments of Internal Medicine and Chemical Pathology, Royal Brisbane and Women’s Hospital, Brisbane, QLD.
  • 6 Diamantina Institute, University of Queensland, Brisbane, QLD.
  • 7 Austin Health, Melbourne, VIC.



We would like to thank Margaret Williamson (National Prescribing Service [NPS]) and Professor Gregory Peterson (University of Tasmania) for their helpful comments regarding study design.

Competing interests:

Jo-anne Brien, Pradeep Jayasuriya, Jennifer Martin and Graeme Vernon were members of the expert panel, and the NPS reimbursed them for time spent on activities for this study.

  • 1. Sharma N, Kerr SJ, Whicker SD. Quality of drug interaction decision support prompts in prescribing software packages for Australian general practice. In: Proceedings of the Australian Health and Medical Research Congress; 2002 Nov 25–29; Melbourne, Vic. Sydney: AH&MR Congress, 2002.
  • 2. Ahearn MD, Kerr SJ. General practitioners’ perceptions of the pharmaceutical decision-support tools in their prescribing software. Med J Aust 2003; 179: 34-37. <MJA full text>
  • 3. National Prescribing Service. Knowledge, attitudes, awareness and behaviour of health professionals: National GP & Pharmacist Surveys. Sydney: NPS, 2006.
  • 4. Ko Y, Abarca J, Malone DC, et al. Practitioners’ views on computerized drug–drug interaction alerts in the VA system. J Am Med Inform Assoc 2007; 14: 56-64.
  • 5. Magnus D, Rodgers S, Avery AJ. GPs’ views on computerized drug interaction alerts: questionnaire survey. J Clin Pharm Ther 2002; 27: 377-382.
  • 6. Spina JR, Glassman PA, Belperio P, et al. Clinical relevance of automated drug alerts from the perspective of medical providers. Am J Med Qual 2005; 20: 7-14.
  • 7. van der Sijs H, Aarts J, Vulto A, Berg M. Overriding of drug safety alerts in computerized physician order entry. J Am Med Inform Assoc 2006; 13: 138-147.
  • 8. Abarca J, Malone DC, Armstrong EP, et al. Concordance of severity ratings provided in four drug interaction compendia. J Am Pharm Assoc 2004; 44: 136-141.
  • 9. Vitry AI. Comparative assessment of four drug interaction compendia. Br J Clin Pharmacol 2007; 63: 709-714.
  • 10. Coiera EW, Westbrook JI. Should clinical software be regulated [editorial]? Med J Aust 2006; 184: 600-601. <MJA full text>
  • 11. Beilby JJ, Duszynski AJ, Wilson A, Turnbull DA. Electronic decision support systems at point of care: trusting the deus ex machina [editorial]. Med J Aust 2005; 183: 99-100. <MJA full text>
  • 12. van Roon EN, Flikweert S, le Comte M, et al. Clinical relevance of drug–drug interactions: a structured assessment procedure. Drug Saf 2005; 28: 1131-1139.
  • 13. McInnes DK, Saltman DC, Kidd MR. General practitioners’ use of computers for prescribing and electronic health records: results from a national survey. Med J Aust 2006; 185: 88-91. <MJA full text>
  • 14. Rossi S, editor. Australian medicines handbook [electronic version]. Adelaide: Australian Medicines Handbook, 2007. (accessed Sep–Nov 2006).
  • 15. MIMS Australia. eMIMS version 5.00.0270. Sydney: CMPMedica Australia, 2006.
  • 16. Baxter K, editor. Stockley’s drug interactions [electronic version]. London: Pharmaceutical Press, 2006. (accessed Sep–Nov 2006).
  • 17. Stockley Editorial Team. Stockley’s interaction alerts [electronic version]. London: Pharmaceutical Press, 2006. (accessed Sep–Nov 2006).
  • 18. Facts & comparisons 3.0. Drug interaction facts [electronic version]. St Louis, Mo: Facts & Comparisons, 2006. (accessed Sep–Nov 2006).
  • 19. Pharmaceutical Pricing Section, Pharmaceutical Benefits Branch. Expenditure and prescriptions twelve months to 30 June 2005. 5B9B1/$File/Full%20book%202004-05.pdf (accessed Aug 2006).
  • 20. Barrons R. Evaluation of personal digital assistant software for drug interactions. Am J Health Syst Pharm 2004; 61: 380-385.
  • 21. Perkins NA, Murphy JE, Malone DC, Armstrong EP. Performance of drug–drug interaction software for personal digital assistants. Ann Pharmacother 2006; 40: 850-855.
  • 22. Abarca J, Colon LR, Wang VS, et al. Evaluation of the performance of drug–drug interaction screening software in community and hospital pharmacies. J Manag Care Pharm 2006; 12: 383-389.
  • 23. Gaikwad R, Sketris I, Shepherd M, Duffy J. Evaluation of accuracy of drug interaction alerts triggered by two electronic medical record systems in primary healthcare. Health Informatics J 2007; 13: 163-177.
  • 24. Bergk V, Gasse C, Schnell R, Haefeli WE. Requirements for a successful implementation of drug interaction information systems in general practice: results of a questionnaire survey in Germany. Eur J Clin Pharmacol 2004; 60: 595-602.


remove_circle_outline Delete Author
add_circle_outline Add Author

Do you have any competing interests to declare? *

I/we agree to assign copyright to the Medical Journal of Australia and agree to the Conditions of publication *
I/we agree to the Terms of use of the Medical Journal of Australia *
Email me when people comment on this article

Online responses are no longer available. Please refer to our instructions for authors page for more information.